Ring Attention and USP: Scaling Transformer Context to Millions of Tokens

def standard_attention_complexity():
    """
    Standard attention memory and compute requirements
    """
    # For a sequence of length N:
    # Memory: O(N²) for attention matrices
    # Compute: O(N²) for attention computation
    
    # Example: 128K context
    sequence_length = 128 * 1024  # 128K
    hidden_dim = 4096
    num_heads = 32
    
    # Attention matrix size: N × N
    attention_matrix = sequence_length ** 2  # ~16 billion entries
    memory_bytes = attention_matrix * 2  # FP16 = 2 bytes
    memory_gb = memory_bytes / (1024**3)  # ~32 GB just for attention!
    
    # This exceeds single GPU memory for most setups

existing_solutions = {
    'flash_attention': {
        'memory': 'O(N) instead of O(N²)',
        'limitation': 'Still single GPU, limited by GPU memory'
    },
    'paged_attention': {
        'memory': 'Reduces fragmentation',
        'limitation': 'Still single GPU'
    },
    'sparse_attention': {
        'memory': 'Reduces computation',
        'limitation': 'Loses information, accuracy trade-off'
    },
    'ring_attention': {
        'memory': 'Distributes across GPUs',
        'limitation': 'No theoretical limit with more GPUs'
    }
}

class RingAttention:
    """
    Ring Attention: Distribute KV cache across GPUs in a ring
    
    Each GPU holds a portion of the KV cache and streams it
    to neighbors as needed during attention computation
    """
    
    def __init__(self, num_gpus, num_heads):
        self.num_gpus = num_gpus
        self.num_heads = num_heads
        self.gpu_id = None
        
    def setup_ring(self, device_id):
        """
        Setup ring topology
        """
        self.gpu_id = device_id
        
        # Each GPU has two neighbors in the ring
        self.prev_gpu = (device_id - 1) % self.num_gpus
        self.next_gpu = (device_id + 1) % self.num_gpus
        
    def forward(self, query, key, value):
        """
        Ring Attention forward pass
        """
        # Split sequence across GPUs
        seq_len = query.size(1)
        chunk_size = seq_len // self.num_gpus
        
        # Each GPU computes attention for its local chunk
        local_query = query[:, chunk_size*self.gpu_id:chunk_size*(self.gpu_id+1)]
        local_key = key[:, chunk_size*self.gpu_id:chunk_size*(self.gpu_id+1)]
        local_value = value[:, chunk_size*self.gpu_id:chunk_size*(self.gpu_id+1)]
        
        # Initialize output
        output = torch.zeros_like(local_query)
        
        # Iterate through all KV chunks in ring order
        for offset in range(self.num_gpus):
            # Compute which KV chunk we need
            kv_gpu = (self.gpu_id + offset) % self.num_gpus
            
            # Fetch KV from that GPU (would be actual communication)
            remote_key = self.fetch_from_gpu(kv_gpu, 'key')
            remote_value = self.fetch_from_gpu(kv_gpu, 'value')
            
            # Compute attention with this chunk
            attn_scores = torch.matmul(local_query, remote_key.transpose(-2, -1))
            attn_scores = attn_scores / (query.size(-1) ** 0.5)
            
            # Apply attention
            chunk_output = torch.matmul(attn_scores, remote_value)
            
            # Accumulate
            output = output + chunk_output
            
            # Pass our KV to next GPU for next iteration
            self.send_to_gpu(self.next_gpu, local_key, 'key')
            self.send_to_gpu(self.next_gpu, local_value, 'value')
        
        return output

import torch
from flash_attn import flash_attn_func

class FlashRingAttention:
    """
    FlashAttention combined with Ring Attention
    """
    
    def __init__(self, num_gpus):
        self.num_gpus = num_gpus
        
    def ring_attention_fused(
        self,
        queries,      # List of query tensors per GPU
        keys,         # List of key tensors per GPU  
        values,       # List of value tensors per GPU
        cu_seqlens    # Cumulative sequence lengths
    ):
        """
        Fused Ring Attention using FlashAttention kernel
        
        Each GPU processes its local queries but needs
        access to all keys/values through the ring
        """
        
        outputs = [None] * self.num_gpus
        
        for gpu_id in range(self.num_gpus):
            q = queries[gpu_id]
            
            # Collect K and V from all GPUs
            # Communication pattern: sequential ring passing
            k_full = []
            v_full = []
            
            for step in range(self.num_gpus):
                source_gpu = (gpu_id + step) % self.num_gpus
                k_full.append(keys[source_gpu])
                v_full.append(values[source_gpu])
            
            k_full = torch.cat(k_full, dim=1)
            v_full = torch.cat(v_full, dim=1)
            
            # Use FlashAttention for local computation
            output = flash_attn_func(q, k_full, v_full, causal=False)
            outputs[gpu_id] = output
        
        return outputs
    
    def ring_attention_streaming(
        self,
        local_query,
        kv_buffer,
        max_chunk_size=4096
    ):
        """
        Streaming version for very long sequences
        
        Instead of waiting for all KV, process in chunks
        while KV streams through the ring
        """
        
        seq_len = local_query.size(1)
        num_chunks = (seq_len + max_chunk_size - 1) // max_chunk_size
        
        output = torch.zeros_like(local_query)
        
        for chunk_idx in range(num_chunks):
            # Get local query chunk
            start = chunk_idx * max_chunk_size
            end = min(start + max_chunk_size, seq_len)
            q_chunk = local_query[:, start:end]
            
            # Accumulate attention from streaming KV
            for _ in range(self.num_gpus):
                # Receive KV from previous GPU
                k_chunk = kv_buffer.receive()
                v_chunk = kv_buffer.receive()
                
                # Compute attention
                attn_chunk = flash_attn_func(q_chunk, k_chunk, v_chunk)
                
                output[:, start:end] += attn_chunk
                
                # Forward KV to next GPU
                kv_buffer.send(k_chunk, v_chunk)
        
        return output

class RingCommunicationOptimizer:
    """
    Optimizing communication in Ring Attention
    """
    
    @staticmethod
    def overlap_compute_communication(
        compute_stream,
        comm_stream,
        key_buffer,
        value_buffer
    ):
        """
        Overlap computation and communication using CUDA streams
        """
        
        # While computing attention for chunk i
        # Simultaneously receive KV for chunk i+1
        
        for i in range(num_chunks):
            # Start async receive for next chunk KV
            if i < num_chunks - 1:
                future_kv = comm_stream.stream_receive(
                    key_buffer, value_buffer,
                    src=gpu_ring[i+1]
                )
            
            # Compute attention for current chunk
            output_chunk = attention_kernel(query_chunk[i], key_chunk[i], value_chunk[i])
            
            # Wait for KV to arrive if needed
            if i < num_chunks - 1:
                future_kv.wait()
            
            # Send current KV to next GPU (async)
            if i > 0:
                comm_stream.async_send(key_chunk[i], value_chunk[i], dst=gpu_ring[i+1])
    
    @staticmethod
    def reduce_communication(
        keys,
        values,
        compression_ratio=4
    ):
        """
        Compress KV before sending through ring
        Reduces bandwidth requirements
        """
        
        # Project to lower dimension
        compressed_keys = keys @ self.compress_proj  # [batch, seq, heads, dim//ratio]
        compressed_values = values @ self.compress_proj
        
        # Send compressed version through ring
        # Decompress on-the-fly when computing attention
        
        return compressed_keys, compressed_values

class UnifiedSequenceParallelism:
    """
    USP combines the best of Ring Attention and DeepSpeed-Ulysses
    
    - Ring Attention: Splits sequence, keeps attention heads together
    - Ulysses: Splits attention heads, combines sequences
    
    USP: Allows both dimensions to be split flexibly
    """
    
    def __init__(self, num_gpus, num_heads, parallelism='hybrid'):
        self.num_gpus = num_gpus
        self.num_heads = num_heads
        self.parallelism = parallelism
        
    def usp_forward(
        self,
        hidden_states,
        attention_mask,
        ring_dim=2,
        ulysses_dim=2
    ):
        """
        USP combines ring and ulysses parallelism
        
        Args:
            ring_dim: How to split along sequence
            ulysses_dim: How to split along attention heads
        """
        
        # Step 1: Sequence splitting (Ring-style)
        # Split hidden states across GPUs along sequence dimension
        hidden_chunks = self.split_sequence(hidden_states, ring_dim)
        
        # Each GPU gets a chunk of the sequence
        local_hidden = hidden_chunks[self.gpu_id]
        
        # Step 2: Attention head splitting (Ulysses-style)
        # Further split heads across GPUs within each sequence chunk
        query, key, value = self.compute_qkv(local_hidden)
        query_heads = self.split_heads(query, ulysses_dim)
        
        # Step 3: All-to-All for attention heads
        # Exchange heads across GPUs while keeping sequence together
        # This is the "Ulysses" part - combines head dimension across GPUs
        query_ulysses = self.all_to_all_heads(query_heads)
        key_ulysses = self.all_to_all_heads(key_heads)
        value_ulysses = self.all_to_all_heads(value_heads)
        
        # Step 4: Local attention computation
        # Now each GPU has a subset of heads for a subset of sequence
        # Attention is LOCAL within each GPU (sequence is "together" per GPU)
        local_output = self.local_attention(
            query_ulysses, key_ulysses, value_ulysses, attention_mask
        )
        
        # Step 5: All-to-All to return heads to original GPUs
        output_heads = self.all_to_all_heads(local_output)
        
        # Step 6: Gather sequence chunks (Ring reverse)
        output = self.gather_sequences(output_heads)
        
        return output
    
    def split_sequence(self, x, num_chunks):
        """Split sequence dimension across GPUs"""
        seq_len = x.size(1)
        chunk_size = seq_len // num_chunks
        
        chunks = []
        for i in range(num_chunks):
            start = i * chunk_size
            end = start + chunk_size if i < num_chunks - 1 else seq_len
            chunks.append(x[:, start:end])
        
        return chunks
    
    def split_heads(self, x, num_splits):
        """Split attention heads across GPUs"""
        batch, seq, hidden = x.shape
        head_dim = hidden // self.num_heads
        
        # Reshape to heads
        x = x.view(batch, seq, self.num_heads, head_dim)
        
        # Split heads
        heads_per_gpu = self.num_heads // num_splits
        return x.split(heads_per_gpu, dim=2)
    
    def all_to_all_heads(self, local_heads):
        """
        All-to-All communication for attention heads
        
        Each GPU sends its local heads to all other GPUs
        and receives a portion of each other's heads
        """
        # Using NCCL all_to_all primitive
        # This is the "Ulysses" transformation
        return torch.distributed.all_to_all(local_heads, group=self.gpu_group)

class AdaptiveUSP:
    """
    Adaptive USP chooses the best parallelism strategy based on workload
    """
    
    def __init__(self, model_config):
        self.model_config = model_config
        
    def select_parallelism(
        self,
        sequence_length,
        num_gpus,
        num_heads,
        memory_per_gpu
    ):
        """
        Automatically select optimal parallelism strategy
        """
        
        # Calculate what's needed
        attention_memory = self.estimate_attention_memory(sequence_length, num_heads)
        ffn_memory = self.estimate_ffn_memory(sequence_length)
        total_memory = attention_memory + ffn_memory
        
        # Decision tree for parallelism
        if total_memory <= memory_per_gpu:
            # Can fit in one GPU, no parallelism needed
            return {'strategy': 'none', 'ring_dim': 1, 'ulysses_dim': 1}
        
        elif attention_memory > memory_per_gpu:
            # Attention is the bottleneck, use Ring
            # Need sequence parallelism
            ring_dim = int(attention_memory / memory_per_gpu) + 1
            ring_dim = min(ring_dim, num_gpus)
            
            return {
                'strategy': 'ring',
                'ring_dim': ring_dim,
                'ulysses_dim': 1
            }
        
        else:
            # FFN is bottleneck, can use Ulysses
            # Split heads across GPUs
            ulysses_dim = num_gpus
            
            return {
                'strategy': 'hybrid',
                'ring_dim': 1,
                'ulysses_dim': ulysses_dim
            }
    
    def hybrid_strategy_selector(
        self,
        sequence_length,
        available_memory,
        network_bandwidth
    ):
        """
        Select optimal hybrid strategy
        """
        
        # Cost models
        ring_comm = self.estimate_ring_comm(sequence_length)
        ulysses_comm = self.estimate_ulysses_comm(sequence_length)
        
        # Network-bound: prefer Ulysses (less communication)
        # Memory-bound: prefer Ring
        
        if network_bandwidth < 100e9:  # < 100 GB/s
            return 'ulysses'  # Less bandwidth
        elif available_memory < 20e9:  # < 20 GB
            return 'ring'  # More memory efficient
        else:
            return 'hybrid'

# Deploy long-context LLM with Ring Attention using vLLM
from vllm import LLM

# Configure for long context
llm = LLM(
    model="meta-llama/Llama-3-70B-128K",
    tensor_parallel_size=8,           # Use 8 GPUs
    max_num_seqs=64,
    max_model_len=128 * 1024,         # 128K context
    enable_chunked_prefill=False,     # Full sequence attention
    # vLLM uses Ring Attention internally for long contexts
)

# Generate with long context
outputs = llm.generate(
    prompts=[
        "Summarize this entire book: [very long text...]"
    ],
    sampling_params=sampling_params
)

import torch
import torch.nn as nn
import torch.distributed as dist

class RingAttentionLayer(nn.Module):
    """
    Complete Ring Attention layer
    """
    
    def __init__(self, hidden_size, num_heads, num_gpus):
        super().__init__()
        
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads
        self.num_gpus = num_gpus
        
        # Standard attention projections
        self.q_proj = nn.Linear(hidden_size, hidden_size)
        self.k_proj = nn.Linear(hidden_size, hidden_size)
        self.v_proj = nn.Linear(hidden_size, hidden_size)
        self.o_proj = nn.Linear(hidden_size, hidden_size)
        
        self.norm = nn.LayerNorm(hidden_size)
        
    def forward(self, x, layer_idx):
        """
        Forward with Ring Attention
        """
        batch_size, seq_len, _ = x.shape
        
        # Compute Q, K, V
        q = self.q_proj(x).view(batch_size, seq_len, self.num_heads, self.head_dim)
        k = self.k_proj(x).view(batch_size, seq_len, self.num_heads, self.head_dim)
        v = self.v_proj(x).view(batch_size, seq_len, self.num_heads, self.head_dim)
        
        # Get current GPU ID
        gpu_id = dist.get_rank()
        
        # Split sequence across GPUs
        chunk_size = seq_len // self.num_gpus
        
        # Local sequence for this GPU
        local_q = q[:, gpu_id*chunk_size:(gpu_id+1)*chunk_size]
        
        # Ring attention: accumulate attention from all KV chunks
        output_chunks = []
        
        # Start with local KV
        current_k = k[:, gpu_id*chunk_size:(gpu_id+1)*chunk_size]
        current_v = v[:, gpu_id*chunk_size:(gpu_id+1)*chunk_size]
        
        for step in range(self.num_gpus):
            # Compute attention with current KV chunk
            attn_scores = torch.matmul(
                local_q,
                current_k.transpose(-2, -1)
            ) / (self.head_dim ** 0.5)
            
            attn_probs = F.softmax(attn_scores, dim=-1)
            output_chunk = torch.matmul(attn_probs, current_v)
            output_chunks.append(output_chunk)
            
            # Receive KV from next GPU in ring
            if step < self.num_gpus - 1:
                next_gpu = (gpu_id + step + 1) % self.num_gpus
                current_k = self.recv_from_gpu(next_gpu, 'key')
                current_v = self.recv_from_gpu(next_gpu, 'value')
        
        # Combine all output chunks
        local_output = torch.cat(output_chunks, dim=1)
        
        # Project and add residual
        output = self.o_proj(local_output)
        output = self.norm(output)
        
        return output + x
    
    def recv_from_gpu(self, src, dtype):
        """Receive tensor from another GPU"""
        # Would use torch.distributed.isend/irecv
        pass
    
    def send_to_gpu(self, dst, tensor, dtype):
        """Send tensor to another GPU"""
        pass

# Benchmark: Time and memory vs context length
benchmarks = {
    'sequence_length': [32K, 64K, 128K, 256K, 512K, 1M],
    
    'ring_attention_8gpu': {
        'memory_gb': [8, 12, 20, 36, 68, 130],
        'time_per_token_ms': [2.1, 2.3, 2.8, 4.2, 7.5, 15.2],
    },
    
    'standard_single_gpu': {
        'memory_gb': [8, 'OOM', 'OOM', 'OOM', 'OOM', 'OOM'],
        'time_per_token_ms': [2.1, 'N/A', 'N/A', 'N/A', 'N/A', 'N/A'],
    },
    
    'usp_8gpu': {
        'memory_gb': [6, 8, 12, 18, 32, 58],
        'time_per_token_ms': [1.8, 1.9, 2.1, 2.8, 4.5, 8.2],
    }
}

# Scaling efficiency with number of GPUs
scaling_results = {
    '2_gpus': {'efficiency': 0.92, 'max_context': '256K'},
    '4_gpus': {'efficiency': 0.88, 'max_context': '512K'},
    '8_gpus': {'efficiency': 0.85, 'max_context': '1M'},
    '16_gpus': {'efficiency': 0.78, 'max_context': '2M'},
    
    # Note: Efficiency decreases due to communication overhead
    # But maximum context increases proportionally
}

use_cases = {
    'document_qa': {
        'context': 'Full books, legal documents',
        'length': '100K - 1M tokens',
        'example': 'Answer questions about entire codebases'
    },
    
    'code_understanding': {
        'context': 'Large code repositories',
        'length': '500K+ tokens',
        'example': 'Understand entire monorepo, cross-file analysis'
    },
    
    'long_conversation': {
        'context': 'Multi-hour chat sessions',
        'length': '128K+ tokens',
        'example': 'AI assistant with perfect long-term memory'
    },
    
    'research_synthesis': {
        'context': 'Reading multiple papers',
        'length': '500K+ tokens',
        'example': 'Synthesize insights from 100s of papers'
    }
}

future_directions = {
    'heterogeneous_systems': {
        'description': 'Mix CPU, GPU, and specialized accelerators',
        'challenge': 'Different memory and compute characteristics'
    },
    
    'hierarchical_ring': {
        'description': 'Multiple rings (within node, across nodes)',
        'challenge': 'Bandwidth differences at different levels'
    },
    
    'adaptive_attention': {
        'description': 'Dynamically choose attention pattern per layer',
        'challenge': 'When to use what pattern'
    },
    
    '1B_context': {
        'description': 'Scale to billion token context',
        'challenge': 'Communication becomes bottleneck'
    }
}